Deep learning has become a popular tool for medical image analysis, but the limited
availability of training data remains a major challenge, particularly in the medical field where …

Deep generative models are a class of techniques that train deep neural networks to model
the distribution of training samples. Research has fragmented into various interconnected …

Generative artificial intelligence (GAI) has emerged as a rapidly burgeoning field
demonstrating significant potential in creating diverse content intelligently and automatically …

Autoencoder networks are unsupervised approaches aiming at combining generative and
representational properties by learning simultaneously an encoder-generator map. Although …

Image inpainting, the process of restoring corrupted images, has seen significant
advancements with the advent of diffusion models (DMs). Despite these advancements …

The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …

Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for
deep generative models. However, learning a VAE from data poses still unanswered …

Representation learning with small labeled data have emerged in many problems, since the
success of deep neural networks often relies on the availability of a huge amount of labeled …

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied
across diverse domains. However, progress is impeded by the scarcity of training data due …

While recent developments in text-to-image generative models have led to a suite of high-
performing methods capable of producing creative imagery from free-form text, there are …